SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education)

• DOI: 10.1007/s00464-023-10288-3
• Publication Date: 2023-07-29T16:01:50Z
• Authors: Jennifer A. Eckhoff; Guy Rosman; Maria S. Altieri; Stefanie Speidel; Danail Stoyanov; Mehran Anvari; Lena Meier-Hein; Keno März; Pierre Jannin; Carla Pugh; Martin Wagner; Elan Witkowski; Paresh Shaw; Amin Madani; Yutong Ban; Thomas Ward; Filippo Filicori; Nicolas Padoy; Mark Talamini; Ozanan R. Meireles
• Keywords: [SDV.IB] Life Sciences [q-bio]/Bioengineering; 0303 health sciences; Consensus; Surgical data science; Data Collection; Surgical AI; Quality Improvement; 3. Good health; Education; Delphi consensus; 2023 SAGES Oral; 03 medical and health sciences; AI; Artificial Intelligence; Humans; [SDV.IB]Life Sciences [q-bio]/Bioengineering

Abstract Background Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose.

Methods Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted.

Results The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data.

Conclusion This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow.